Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study DOI Creative Commons
Jiahao Wu,

Yongkai Zhao,

Ruihan Zhang

и другие.

Solar Compass, Год журнала: 2024, Номер 12, С. 100089 - 100089

Опубликована: Сен. 17, 2024

Язык: Английский

A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods DOI
Chu Zhang, Yuhan Wang,

Yongyan Fu

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 301, С. 118045 - 118045

Опубликована: Янв. 5, 2024

Язык: Английский

Процитировано

23
Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network DOI

Jianhua Zhu,

Yaoyao He, Xiaodong Yang

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 301, С. 118062 - 118062

Опубликована: Янв. 13, 2024

Язык: Английский

Процитировано

22
A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism DOI
Xiuying Yan,

Xingxing Ji,

Qinglong Meng

и другие.

Energy, Год журнала: 2024, Номер 292, С. 130388 - 130388

Опубликована: Янв. 23, 2024

Язык: Английский

Процитировано

11
SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples DOI

Xuejian Yao,

Xingchi Lu,

Quan Jiang

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 61, С. 102560 - 102560

Опубликована: Апрель 24, 2024

Язык: Английский

Процитировано

11
Wind and Solar Power Generation Forecasting Based on Hybrid CNN-ABiLSTM, CNN-Transformer-MLP Models DOI
Tasarruf Bashir,

Huifang Wang,

Mustafa Tahir

и другие.

Renewable Energy, Год журнала: 2024, Номер unknown, С. 122055 - 122055

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

9
Combined Ultra-Short-Term Photovoltaic Power Prediction Based on CEEMDAN Decomposition and RIME Optimized AM-TCN-BiLSTM DOI

Daixuan Zhou,

Yujin Liu,

Xu Wang

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 134847 - 134847

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning DOI

Jianqiang Gong,

Zhiguo Qu, Zhiyu Zhu

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135286 - 135286

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration DOI
Yongning Zhao, Shiji Pan, Yuan Zhao

и другие.

Energy, Год журнала: 2023, Номер 288, С. 129847 - 129847

Опубликована: Дек. 1, 2023

Язык: Английский

Процитировано

19
Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm DOI Creative Commons
Hongbin Sun,

Qing Cui,

Jingya Wen

и другие.

Energy Reports, Год журнала: 2024, Номер 11, С. 6186 - 6198

Опубликована: Июнь 1, 2024

To improve the consumption rate of renewable energy and ensure stability power heat supply within industrial park, a park-integrated system (PIES), which incorporates electric heating equipment, storage devices, multiple micro sources, is established. Based on established PIES, price-based demand response (PDR) mechanism introduced to establish multi-objective optimization scheduling model with two time scales: day-ahead intra-day scheduling, aiming minimize impact main grid flexibility operation real-time scheduling. With objectives minimizing operating costs maximizing environmental benefits for hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), combines Genetic Algorithm II (NSGA-II) (BWO) proposed solve problem model. Simultaneously, an adaptive penalty function in handle constraint problems additional term wind solar curtailment included constraints sustainable energy. The NSBWO exhibits more robust search capabilities faster convergence speed than NSGA-II MOPSO algorithm. Simulation results show that can achieve low-carbon economic both long-term short-term scales consumption. Due its flexibility, has lower

Язык: Английский

Процитировано

6
Multimodal deep learning water level forecasting model for multiscale drought alert in Feiyun River basin DOI
Rui Dai, Wanliang Wang, Zhang Ren-gong

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 244, С. 122951 - 122951

Опубликована: Дек. 15, 2023

Язык: Английский

Процитировано

13